Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "59" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 43 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 41 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459858 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 27.201683 | 1.551202 | -0.791642 | 0.865188 | 0.291481 | 2.403911 | 3.708576 | 5.056602 | 0.6547 | 0.6945 | 0.3951 | 2.483735 | 2.420656 |
| 2459857 | digital_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 2.431043 | -0.630875 | -1.325867 | -0.642617 | 1.134683 | 0.733186 | -0.612285 | 1.017405 | 0.0288 | 0.0266 | 0.0013 | nan | nan |
| 2459856 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 36.872522 | 4.123114 | 1.390223 | 1.187818 | 2.715057 | 0.874903 | 4.353870 | 6.058571 | 0.6456 | 0.7049 | 0.3876 | 2.617801 | 2.510649 |
| 2459855 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 36.769955 | 5.263141 | 1.362815 | 0.624491 | 1.049044 | 1.398526 | 2.532185 | 3.828587 | 0.6363 | 0.7165 | 0.4213 | 2.664733 | 2.585901 |
| 2459854 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 43.721341 | 4.703118 | 1.820185 | 0.095387 | 0.915799 | 0.913826 | 1.051646 | 1.714877 | 0.6480 | 0.7450 | 0.4263 | 2.443960 | 2.630338 |
| 2459853 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 30.645108 | 3.140462 | 2.434389 | 0.250182 | 2.991383 | 0.566680 | 4.194870 | 5.984501 | 0.6741 | 0.6953 | 0.3995 | 2.703891 | 2.743787 |
| 2459852 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 19.368184 | 3.246582 | 2.186928 | 0.127206 | 5.347286 | 0.468716 | 5.050586 | 0.591528 | 0.7842 | 0.8461 | 0.2432 | 5.201215 | 4.633717 |
| 2459851 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 21.408526 | 1.992630 | 2.651270 | 0.392886 | 7.762365 | 1.898464 | 5.350345 | 3.116609 | 0.7071 | 0.7542 | 0.3358 | 2.799971 | 2.907872 |
| 2459850 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 26.038955 | 3.197912 | 2.226979 | 0.241278 | 3.710470 | 0.146991 | 2.885598 | 2.037742 | 0.0457 | 0.0488 | 0.0014 | 1.182413 | 1.178893 |
| 2459849 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 38.165788 | 3.916434 | 5.994384 | 1.500904 | 3.409707 | 1.421007 | 6.934457 | 9.013549 | 0.0469 | 0.0544 | 0.0014 | 1.173547 | 1.169185 |
| 2459848 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 24.505006 | 3.267827 | 1.917040 | 2.352809 | 3.438660 | -0.004557 | 4.790111 | 4.921548 | 0.0508 | 0.0543 | 0.0018 | 1.201411 | 1.201962 |
| 2459847 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 34.772840 | 3.457578 | 2.228304 | 1.715500 | 4.486032 | 1.567689 | 5.637892 | 4.807589 | 0.0408 | 0.0492 | 0.0015 | 1.159278 | 1.157740 |
| 2459846 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 27.210792 | 3.531416 | 1.010270 | 1.399444 | 4.948944 | 2.089112 | 4.175592 | 3.060530 | 0.0527 | 0.0555 | 0.0026 | 1.169073 | 1.178312 |
| 2459845 | digital_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 23.526629 | 5.538594 | 2.335410 | 2.737583 | 3.733709 | 1.254090 | 22.658554 | 10.241235 | 0.7171 | 0.7530 | 0.3493 | 4.915205 | 5.549201 |
| 2459844 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 4.262356 | 1.219994 | 6.765837 | 1.487968 | 1.107066 | 1.292099 | -0.687745 | 0.549580 | 0.0288 | 0.0259 | 0.0021 | nan | nan |
| 2459843 | digital_maintenance | 100.00% | 0.66% | 0.66% | 0.00% | 100.00% | 0.00% | 1.270081 | 19.158184 | 10.781159 | -0.029407 | 3.770363 | 1.333786 | 4.581501 | 24.254117 | 0.7635 | 0.7336 | 0.3715 | 4.834336 | 3.320700 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 27.201683 | 1.551202 | 27.201683 | 0.865188 | -0.791642 | 2.403911 | 0.291481 | 5.056602 | 3.708576 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 2.431043 | -0.630875 | 2.431043 | -0.642617 | -1.325867 | 0.733186 | 1.134683 | 1.017405 | -0.612285 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 36.872522 | 36.872522 | 4.123114 | 1.390223 | 1.187818 | 2.715057 | 0.874903 | 4.353870 | 6.058571 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 36.769955 | 5.263141 | 36.769955 | 0.624491 | 1.362815 | 1.398526 | 1.049044 | 3.828587 | 2.532185 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 43.721341 | 4.703118 | 43.721341 | 0.095387 | 1.820185 | 0.913826 | 0.915799 | 1.714877 | 1.051646 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 30.645108 | 3.140462 | 30.645108 | 0.250182 | 2.434389 | 0.566680 | 2.991383 | 5.984501 | 4.194870 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 19.368184 | 19.368184 | 3.246582 | 2.186928 | 0.127206 | 5.347286 | 0.468716 | 5.050586 | 0.591528 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 21.408526 | 21.408526 | 1.992630 | 2.651270 | 0.392886 | 7.762365 | 1.898464 | 5.350345 | 3.116609 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 26.038955 | 26.038955 | 3.197912 | 2.226979 | 0.241278 | 3.710470 | 0.146991 | 2.885598 | 2.037742 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 38.165788 | 38.165788 | 3.916434 | 5.994384 | 1.500904 | 3.409707 | 1.421007 | 6.934457 | 9.013549 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 24.505006 | 3.267827 | 24.505006 | 2.352809 | 1.917040 | -0.004557 | 3.438660 | 4.921548 | 4.790111 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 34.772840 | 3.457578 | 34.772840 | 1.715500 | 2.228304 | 1.567689 | 4.486032 | 4.807589 | 5.637892 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 27.210792 | 27.210792 | 3.531416 | 1.010270 | 1.399444 | 4.948944 | 2.089112 | 4.175592 | 3.060530 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Shape | 23.526629 | 5.538594 | 23.526629 | 2.737583 | 2.335410 | 1.254090 | 3.733709 | 10.241235 | 22.658554 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | ee Power | 6.765837 | 4.262356 | 1.219994 | 6.765837 | 1.487968 | 1.107066 | 1.292099 | -0.687745 | 0.549580 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | N05 | digital_maintenance | nn Temporal Discontinuties | 24.254117 | 19.158184 | 1.270081 | -0.029407 | 10.781159 | 1.333786 | 3.770363 | 24.254117 | 4.581501 |